Different types of data storage incur different costs, with faster storage costing more per gigabyte than slower storage. One approach to managing storage costs is to use hierarchical or “multi-temperature storage,” where frequently accessed (aka “hot”) data are stored on faster but more expensive storage (e.g. solid-state “disks”), and less frequently accessed data are moved to progressively slower but cheaper storage (e.g. physical hard disks, tape drives, etc.).
Specific database applications or users also exhibit different workload patterns. Some make high-frequency access to data while others may make less frequent data accesses. In another approach, workload management techniques perform a similar task to multi-temperature storage in that they provide a mechanism to give different workloads differential levels of access to resources. For example, giving a high-priority workload a larger share of available CPU time than other workloads is similar qualitatively to giving a given subset of data residence on a faster backing storage device.
However, the above approaches require user intervention to determine which data or workloads should get proportionally more access to the fast and expensive resources, and which should be relegated more to the slower and cheaper resources.